Detection of dawn sea fog/low stratus using geostationary satellite imagery

Traditional satellite-based detection of dawn sea fog/low stratus (SFLS) is difficult because of the weak reflectivity in the visible at low solar elevation angles and the contamination of the reflected sunlight in the mid-infrared. Here, based on single geostationary satellite measurements acquired by China's Fengyun 4A (FY-4A), we propose a dawn SFLS detection algorithm using the joint Fully Convolutional Network and Conditional Random Field (FCN-CRF), which are well known for image semantic segmentation under low contrast conditions. We train the FCN-CRF detection algorithm using FY-4A measurements over the Yellow Sea, where some dawn SFLS events are long-lived, providing relatively time-invariant dawn SFLS samples for training. We design a SFLS labelling technique using the satellite observations before and after dawn to train the FCN-CRF detection for dawn SFLS. A test against buoy visibility observations shows that the FCN-CRF detection is able to detect dawn SFLS with satisfactory accuracy, with a probability of detection (POD) of 84.9%, a false alarm ratio (FAR) of 8.7%, a critical success index (CSI) of 78.5% and a hit rate score (HR) of 87.4%. •Sea fog will last during dawn period if there is fog before or after the dawn.•A novel dawn sea fog sample labels extracting method for FCN training.•Dawn Sea fog detection using the Fully Convolutional Network and Conditional Random Field.